{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright 2021 The TensorFlow Similarity Authors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# @title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TensorFlow Similarity Supervised Learning Hello World"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/similarity/blob/master/examples/supervised_hello_world.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://github.com/tensorflow/similarity/blob/master/examples/supervised_hello_world.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[TensorFlow Similarity](https://github.com/tensorflow/similarity) is a python package focused on making similarity learning quick and easy. \n",
    "\n",
    "### Notebook goal\n",
    "\n",
    "This notebook demonstrates how to use TensorFlow Similarity to train a `SimilarityModel()` on a fraction of the MNIST classes, and yet the model is able to index and retrieve similar looking images for all MNIST classes. \n",
    "\n",
    "You are going to learn about the main features offered by the `SimilarityModel()` and will:\n",
    "\n",
    " 1. `train()` a similarity model on a sub-set of the 10 MNIST classes that will learn how to project digits within a cosine space\n",
    "\n",
    " 2. `index()` a few examples of each of the 10 classes present in the train dataset (e.g 10 images per classes) to make them searchable\n",
    "\n",
    " 3. `lookup()` a few test images to check that the trained model, despite having only a few examples of seen and unseen classes in it's index, is able to efficiently retrieve similar looking examples for all classes.\n",
    "\n",
    " 4. `calibrate()` the model to estimate what is the best distance theshold to separate matching elements from elements belonging to other classes.\n",
    "\n",
    " 5. `match()` the test dataset to evaluate how well the calibrated model works for classification purpose.\n",
    "\n",
    "### Things to try \n",
    "\n",
    "Along the way you can try the following things to improve the model performance:\n",
    "- Adding more \"seen\" classes at training time.\n",
    "- Use a larger embedding by increasing the size of the output.\n",
    "- Add data augmentation pre-processing layers to the model.\n",
    "- Include more examples in the index to give the models more points to choose from.\n",
    "- Try a more challenging dataset, such as Fashion MNIST."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gc\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from tabulate import tabulate\n",
    "\n",
    "# INFO messages are not printed.\n",
    "# This must be run before loading other modules.\n",
    "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# install TF similarity if needed\n",
    "try:\n",
    "    import tensorflow_similarity as tfsim  # main package\n",
    "except ModuleNotFoundError:\n",
    "    !pip install tensorflow_similarity\n",
    "    import tensorflow_similarity as tfsim\n",
    "import tensorflow_similarity.visualization as tfsim_visualization\n",
    "import tensorflow_similarity.losses as tfsim_losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfsim.utils.tf_cap_memory()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clear out any old model state.\n",
    "gc.collect()\n",
    "tf.keras.backend.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow: 2.12.0\n",
      "TensorFlow Similarity 0.18.0.dev8\n"
     ]
    }
   ],
   "source": [
    "print(\"TensorFlow:\", tf.__version__)\n",
    "print(\"TensorFlow Similarity\", tfsim.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation\n",
    "\n",
    "We are going to load the MNIST dataset and restrict our training data to **only N of the 10 classes** (6 by default) to showcase how the model is able to find similar examples from classes unseen during training. The model's ability to generalize the matching to unseen classes, without retraining, is one of the main reason you would want to use metric learning.\n",
    "\n",
    "\n",
    "**WARNING**: Tensorflow similarity expects `y_train` to be an IntTensor containing the class ids for each example instead of the standard categorical encoding traditionally used for multi-class classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a similarity model to learn efficiently, each batch must contains at least 2 examples of each class.\n",
    "\n",
    "To make this easy, tf_similarity offers `Samplers()` that enable you to set both the number of classes and the minimum number of examples of each class per batch. Here we are creating a `TFDataSampler()` which allows you to sample from a `tf.data.Dataset`  and provides multiple examples per class.\n",
    "\n",
    "TensorFlow Similarity provides various samplers to accomodate different requirements, including a `MultiShotMemorySampler()` for simple, single headed in-memory datsets, `SingleShotMemorySampler()` for single-shot learning, a `TFDatasetMultiShotMemorySampler()` that integrate directly with the TensorFlow datasets catalogue, and a `TFRecordDatasetSampler()` that allows you to sample from very large datasets stored on disk as TFRecords shards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "CLASSES = [2, 3, 1, 7, 9, 6, 8, 5, 0, 4]\n",
    "NUM_CLASSES = 6  # @param {type: \"slider\", min: 1, max: 10}\n",
    "CLASSES_PER_BATCH = NUM_CLASSES\n",
    "EXAMPLES_PER_CLASS = 10  # @param {type:\"integer\"}\n",
    "STEPS_PER_EPOCH = 1000  # @param {type:\"integer\"}\n",
    "\n",
    "sampler = tfsim.samplers.TFDataSampler(\n",
    "    ds=tf.data.Dataset.from_tensor_slices((x_train, y_train)),\n",
    "    classes_per_batch=CLASSES_PER_BATCH,\n",
    "    examples_per_class_per_batch=EXAMPLES_PER_CLASS,\n",
    "    class_list=CLASSES[:NUM_CLASSES],  # Only use the first 6 classes for training.\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model definition\n",
    "\n",
    "`SimilarityModel()` models extend `tensorflow.keras.model.Model` with additional features and functionality that allow you to index and search for similar looking examples.\n",
    "\n",
    "As visible in the model definition below, similarity models output a 64 dimensional float embedding using the `MetricEmbedding()` layers. This layer is a Dense layer with L2 normalization. Thanks to the loss, the model learns to minimize the distance between similar examples and maximize the distance between dissimilar examples. As a result, the distance between examples in the embedding space is meaningful; the smaller the distance the more similar the examples are. \n",
    "\n",
    "Being able to use a distance as a meaningful proxy for how similar two examples are, is what enables the fast ANN (aproximate nearest neighbor) search. Using a sub-linear ANN search instead of a standard quadratic NN search is what allows deep similarity search to scale to millions of items. The built in memory index used in this notebook scales to a million indexed examples very easily... if you have enough RAM :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"similarity_model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 28, 28, 1)]       0         \n",
      "                                                                 \n",
      " rescaling (Rescaling)       (None, 28, 28, 1)         0         \n",
      "                                                                 \n",
      " conv2d (Conv2D)             (None, 26, 26, 32)        320       \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 24, 24, 32)        9248      \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 12, 12, 32)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 10, 10, 64)        18496     \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 8, 8, 64)          36928     \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 4096)              0         \n",
      "                                                                 \n",
      " metric_embedding (MetricEmb  (None, 64)               262208    \n",
      " edding)                                                         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 327,200\n",
      "Trainable params: 327,200\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def get_model():\n",
    "    inputs = tf.keras.layers.Input(shape=(28, 28, 1))\n",
    "    x = tf.keras.layers.Rescaling(1.0 / 255.0)(inputs)\n",
    "    x = tf.keras.layers.Conv2D(32, 3, activation=\"relu\")(x)\n",
    "    x = tf.keras.layers.Conv2D(32, 3, activation=\"relu\")(x)\n",
    "    x = tf.keras.layers.MaxPool2D()(x)\n",
    "    x = tf.keras.layers.Conv2D(64, 3, activation=\"relu\")(x)\n",
    "    x = tf.keras.layers.Conv2D(64, 3, activation=\"relu\")(x)\n",
    "    x = tf.keras.layers.Flatten()(x)\n",
    "    # smaller embeddings will have faster lookup times while a larger embedding will improve the accuracy up to a point.\n",
    "    outputs = tfsim.layers.MetricEmbedding(64)(x)\n",
    "    return tfsim.models.SimilarityModel(inputs, outputs)\n",
    "\n",
    "\n",
    "model = get_model()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loss definition\n",
    "\n",
    "Overall what makes Metric losses different from tradional losses is that:\n",
    "- **They expect different inputs.** Instead of having the prediction equal the true values, they expect embeddings as `y_preds` and the id (as an int32) of the class as `y_true`. \n",
    "- **They require a distance.** You need to specify which `distance` function to use to compute the distance between embeddings. `cosine` is usually a great starting point and the default.\n",
    "\n",
    "In this example we are using the `MultiSimilarityLoss()`. This loss takes a weighted combination of all valid positive and negative pairs, making it one of the best loss that you can use for similarity training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "distance = \"cosine\"  # @param [\"cosine\", \"L2\", \"L1\"]{allow-input: false}\n",
    "loss = tfsim_losses.MultiSimilarityLoss(distance=distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compilation\n",
    "\n",
    "Tensorflow similarity use an extended `compile()` method that allows you to optionally specify `distance_metrics` (metrics that are computed over the distance between the embeddings), and the distance to use for the indexer.\n",
    "\n",
    "By default the `compile()` method tries to infer what type of distance you are using by looking at the first loss specified. If you use multiple losses, and the distance loss is not the first one, then you need to specify the distance function used as `distance=` parameter in the compile function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distance metric automatically set to cosine use the distance arg to override.\n"
     ]
    }
   ],
   "source": [
    "LR = 0.000005  # @param {type:\"number\"}\n",
    "model.compile(optimizer=tf.keras.optimizers.Adam(LR), loss=loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training\n",
    "\n",
    "Similarity models are trained like normal models. \n",
    "\n",
    "**NOTE**: don't expect the validation loss to decrease too much here because we only use a subset of the classes within the train data but include all classes in the validation data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1000/1000 [==============================] - 6s 4ms/step - loss: 0.8091 - val_loss: 0.4332\n",
      "Epoch 2/10\n",
      "1000/1000 [==============================] - 4s 4ms/step - loss: 0.5205 - val_loss: 0.4056\n",
      "Epoch 3/10\n",
      "1000/1000 [==============================] - 4s 4ms/step - loss: 0.3956 - val_loss: 0.3885\n",
      "Epoch 4/10\n",
      "1000/1000 [==============================] - 4s 4ms/step - loss: 0.3164 - val_loss: 0.3787\n",
      "Epoch 5/10\n",
      "1000/1000 [==============================] - 4s 4ms/step - loss: 0.2721 - val_loss: 0.3687\n",
      "Epoch 6/10\n",
      "1000/1000 [==============================] - 4s 4ms/step - loss: 0.2357 - val_loss: 0.3649\n",
      "Epoch 7/10\n",
      "1000/1000 [==============================] - 4s 4ms/step - loss: 0.2062 - val_loss: 0.3579\n",
      "Epoch 8/10\n",
      "1000/1000 [==============================] - 4s 4ms/step - loss: 0.1881 - val_loss: 0.3547\n",
      "Epoch 9/10\n",
      "1000/1000 [==============================] - 4s 4ms/step - loss: 0.1676 - val_loss: 0.3493\n",
      "Epoch 10/10\n",
      "1000/1000 [==============================] - 4s 4ms/step - loss: 0.1581 - val_loss: 0.3514\n"
     ]
    }
   ],
   "source": [
    "EPOCHS = 10  # @param {type:\"integer\"}\n",
    "history = model.fit(sampler, steps_per_epoch=STEPS_PER_EPOCH, epochs=EPOCHS, validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# expect loss: 0.14 / val_loss: 0.33\n",
    "plt.plot(history.history[\"loss\"])\n",
    "plt.plot(history.history[\"val_loss\"])\n",
    "plt.legend([\"loss\", \"val_loss\"])\n",
    "plt.title(f\"Loss: {loss.name} - LR: {LR}\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexing\n",
    "\n",
    "Indexing is where things get different from traditional classification models. Because the model learned to output an embedding that represent the example position within the learned metric space, we need a way to find which known example(s) are the closest to determine the class of the query example (aka nearest neighbors classication).\n",
    "\n",
    "To do so, **we are creating an index of known examples from all the classes present in the dataset**. We do this by taking a total of **200 examples from the train dataset which amount to 20 examples per class** and we use the `index()` method of the model to build the index.\n",
    "\n",
    "we store the images (x_index) as data in the index `(data=x_index)` so that we can display them later. Here the images are small so its not an issue but in general, be careful while storing a lot of data in the index to avoid blewing up your memory. You might consider using a different `Store()` backend if you have to store and serve very large indexes.\n",
    "\n",
    "Indexing more examples per class will help increase the accuracy/generalization, as having more variations improves the classifier \"knowledge\" of what variations to expect. \n",
    "\n",
    "Reseting the index is not needed for the first run; however we always calling it to ensure we start the evaluation with a clean index in case of a partial re-run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9a1ac1ced839469d9b2a89996308434b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "filtering examples:   0%|          | 0/60000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ff777c6324b24c60be789ae3f015bef7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "selecting classes:   0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d3c858986ea1462badb4ea4f0e00136e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "gather examples:   0%|          | 0/200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Indexing 200 points]\n",
      "|-Computing embeddings\n",
      "7/7 [==============================] - 0s 4ms/step\n",
      "|-Storing data points in key value store\n"
     ]
    }
   ],
   "source": [
    "x_index, y_index = tfsim.samplers.select_examples(x_train, y_train, CLASSES, 20)\n",
    "model.reset_index()\n",
    "model.index(x_index, y_index, data=x_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Querying\n",
    "\n",
    "To \"classify\" examples, we need to lookup their *k* [nearest neighbors](https://scikit-learn.org/stable/modules/neighbors.html) in the index.\n",
    "\n",
    "Here we going to query a single random example for each class from the test dataset using `select_examples()` and then find their nearest neighbors using the `lookup()` function.\n",
    "\n",
    "**NOTE** By default the classes 8, 5, 0, and 4 were not seen during training, but we still get reasonable matches as visible in the image below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "18825ad345ac4bd5a72045968807038a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "filtering examples:   0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c3e1fa1c16804c17a3af943b63cd23db",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "selecting classes:   0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4198dea000294a9d8cac2dc51e096227",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "gather examples:   0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 26ms/step\n",
      "\n",
      "Performing NN search\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "71400dcb5a634822b9b9b07d3b3db3c0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Building NN list:   0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# re-run to test on other examples\n",
    "num_neighbors = 5\n",
    "\n",
    "# select\n",
    "x_display, y_display = tfsim.samplers.select_examples(x_test, y_test, CLASSES, 1)\n",
    "\n",
    "# lookup nearest neighbors in the index\n",
    "nns = model.lookup(x_display, k=num_neighbors)\n",
    "\n",
    "# display\n",
    "for idx in np.argsort(y_display):\n",
    "    tfsim_visualization.viz_neigbors_imgs(x_display[idx], y_display[idx], nns[idx], fig_size=(16, 2), cmap=\"Greys\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calibration\n",
    "\n",
    "To be able to tell if an example matches a given class, we first need to `calibrate()` the model to find the optimal cut point. This cut point is the maximum distance below which returned neighbors are of the same class. Increasing the threshold improves the recall at the expense of the precision.\n",
    "\n",
    "By default, the calibration uses the F-score classification metric to optimally balance out the precsion and recalll; however, you can speficy your own target and change the calibration metric to better suite your usecase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 0s 846us/step\n",
      "\n",
      "Performing NN search\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d37e9967a35a4ad88ba4a147d309840e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Building NN list:   0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c55c960ec91b4d19b49e5a9ec806ff1c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Evaluating:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " name        value    distance    precision    recall    binary_accuracy        f1\n",
      "-------  --------  ----------  -----------  --------  -----------------  --------\n",
      "optimal  0.965625    0.363853     0.938259  0.994635              0.927  0.965625\n"
     ]
    }
   ],
   "source": [
    "num_calibration_samples = 1000  # @param {type:\"integer\"}\n",
    "calibration = model.calibrate(\n",
    "    x_train[:num_calibration_samples],\n",
    "    y_train[:num_calibration_samples],\n",
    "    extra_metrics=[\"precision\", \"recall\", \"binary_accuracy\"],\n",
    "    verbose=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metrics ploting\n",
    "\n",
    "Let's plot the performance metrics to see how they evolve as the distance threshold increases. \n",
    "\n",
    "We clearly see an inflection point where the precision and recall intersect, however, this is not the `optimal_cutpoint` because the recall continues to increase faster than the precision decreases. Different usecases will have different performance profiles, which why each model needs to be calibrated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "x = calibration.thresholds[\"distance\"]\n",
    "ax.plot(x, calibration.thresholds[\"precision\"], label=\"precision\")\n",
    "ax.plot(x, calibration.thresholds[\"recall\"], label=\"recall\")\n",
    "ax.plot(x, calibration.thresholds[\"f1\"], label=\"f1 score\")\n",
    "ax.legend()\n",
    "ax.set_title(\"Metric evolution as distance increase\")\n",
    "ax.set_xlabel(\"Distance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precision/Recall curve\n",
    "\n",
    "We can see in the precision/recall curve below, that the curve is not smooth.\n",
    "This is because the recall can improve independently of the precision causing a \n",
    "seesaw pattern.\n",
    "\n",
    "Additionally, the model does extremly well on known classes and less well on \n",
    "the unseen ones, which contributes to the flat curve at the begining followed \n",
    "by a sharp decline as the distance threshold increases and \n",
    "examples are further away from the indexed examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(calibration.thresholds[\"recall\"], calibration.thresholds[\"precision\"])\n",
    "ax.set_title(\"Precision recall curve\")\n",
    "ax.set_xlabel(\"Recall\")\n",
    "ax.set_ylabel(\"Precision\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matching\n",
    "\n",
    "The purpose of `match()` is to allow you to use your similarity models to make \n",
    "classification predictions. It accomplishes this by finding the nearest neigbors\n",
    "for a set of query examples and returning an infered label based on neighbors \n",
    "labels and the matching strategy used (MatchNearest by default).\n",
    "\n",
    "Note: unlike traditional models, the  `match()` method potentially returns -1 \n",
    "when there are no indexed examples below the cutpoint threshold. The -1 class\n",
    "should be treated as \"unknown\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matching in practice\n",
    "Let's now match a 10 examples to see how you can use the model `match()` method \n",
    "in practice. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 11ms/step\n",
      "  Predicted    Expected    Correct\n",
      "-----------  ----------  ---------\n",
      "          7           7          1\n",
      "          2           2          1\n",
      "          1           1          1\n",
      "          0           0          1\n",
      "          4           4          1\n",
      "          1           1          1\n",
      "          4           4          1\n",
      "          9           9          1\n",
      "          6           5          0\n",
      "          9           9          1\n"
     ]
    }
   ],
   "source": [
    "num_matches = 10  # @param {type:\"integer\"}\n",
    "\n",
    "matches = model.match(x_test[:num_matches], cutpoint=\"optimal\")\n",
    "rows = []\n",
    "for idx, match in enumerate(matches):\n",
    "    rows.append([match, y_test[idx], match == y_test[idx]])\n",
    "print(tabulate(rows, headers=[\"Predicted\", \"Expected\", \"Correct\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### confusion matrix\n",
    "Now that we have a better sense of what the match() method does, let's scale up \n",
    "to a few thousand samples per class and evaluate how good our model is at \n",
    "predicting the correct classes.\n",
    "\n",
    "As expected, while the model prediction performance is very good, its not \n",
    "competitive with a classification model. However this lower accuracy comes with \n",
    "the unique advantage that the model is able to classify classes \n",
    "that were not seen during training.\n",
    "\n",
    "\n",
    "**NOTE** `tf.math.confusion_matrix` doesn't support negative classes, so we are going to use **class 10 as our unknown class**. As mentioned earlier, unknown examples are \n",
    "any testing example for which the closest neighbor distance is greater than the cutpoint threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "79daa753b5a74565bd5783a62ab0be0b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "filtering examples:   0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "63e710f7d247453e9143054d67777e48",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "selecting classes:   0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "69e723f90ef04b1ab554abcbdacd06bb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "gather examples:   0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# used to label in images in the viz_neighbors_imgs plots\n",
    "# note we added a 11th classes for unknown\n",
    "labels = [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"Unknown\"]\n",
    "num_examples_per_class = 1000\n",
    "cutpoint = \"optimal\"\n",
    "\n",
    "x_confusion, y_confusion = tfsim.samplers.select_examples(x_test, y_test, CLASSES, num_examples_per_class)\n",
    "\n",
    "matches = model.match(x_confusion, cutpoint=cutpoint, no_match_label=10)\n",
    "cm = tfsim_visualization.confusion_matrix(\n",
    "    matches,\n",
    "    y_confusion,\n",
    "    labels=labels,\n",
    "    title=\"Confusin matrix for cutpoint:%s\" % cutpoint,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Index information\n",
    "\n",
    "Following `model.summary()` you can get information about the index configuration and its performance using `index_summary()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info]\n",
      "------------------  ------------\n",
      "distance            cosine\n",
      "key value store     MemoryStore\n",
      "search algorithm    LinearSearch\n",
      "evaluator           memory\n",
      "index size          200\n",
      "calibrated          True\n",
      "calibration_metric  f1\n",
      "embedding_output\n",
      "------------------  ------------\n",
      "\n",
      "\n",
      "\n",
      "[Performance]\n",
      "-----------  ---------------\n",
      "num lookups  11020\n",
      "min              0.000378548\n",
      "max              0.000378548\n",
      "avg              0.000378548\n",
      "median           0.000378548\n",
      "stddev           1.0842e-19\n",
      "-----------  ---------------\n"
     ]
    }
   ],
   "source": [
    "model.index_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving and reloading\n",
    "Saving and reloading the model works as you would expected: \n",
    "\n",
    "- `model.save(path, save_index=True)`: save the model and the index on disk. By default the index is compressed but this can be disabled by setting `compressed=False`\n",
    "\n",
    "- `model = tf.keras.model.load_model(path, custom_objects={\"SimilarityModel\": tfsim.models.SimilarityModel})` reload the model. \n",
    "\n",
    "- **NOTE**: We need to pass `SimilarityModel` as a custom object to ensure that Keras knows about the index methods.\n",
    "\n",
    "- `model.load_index(path)` Is requried to reload the index. \n",
    "\n",
    "- `model.save_index(path)` and `model.load_index(path)` allows to save/reload an index independently of saving/loading a model.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: models/hello_world/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: models/hello_world/assets\n"
     ]
    }
   ],
   "source": [
    "# save the model and the index\n",
    "save_path = \"models/hello_world\"  # @param {type:\"string\"}\n",
    "model.save(save_path, save_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reloading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distance metric automatically set to cosine use the distance arg to override.\n",
      "Loading index data\n",
      "loaded 200 records from models/hello_world/index/store\n",
      "Loading search index\n",
      "Loading calibration data\n"
     ]
    }
   ],
   "source": [
    "# reload the model\n",
    "reloaded_model = tf.keras.models.load_model(\n",
    "    save_path,\n",
    "    custom_objects={\"SimilarityModel\": tfsim.models.SimilarityModel},\n",
    ")\n",
    "# reload the index\n",
    "reloaded_model.load_index(save_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info]\n",
      "------------------  ------------\n",
      "distance            cosine\n",
      "key value store     MemoryStore\n",
      "search algorithm    LinearSearch\n",
      "evaluator           memory\n",
      "index size          200\n",
      "calibrated          True\n",
      "calibration_metric  f1\n",
      "embedding_output\n",
      "------------------  ------------\n",
      "\n",
      "\n",
      "\n",
      "[Performance]\n",
      "-----------  -\n",
      "num lookups  0\n",
      "min          0\n",
      "max          0\n",
      "avg          0\n",
      "median       0\n",
      "stddev       0\n",
      "-----------  -\n"
     ]
    }
   ],
   "source": [
    "# check the index is back\n",
    "reloaded_model.index_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Query reloaded model\n",
    "Querying the reloaded model with its reload index works as expected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5cd6cb4220f04b5b8368b12434491aa8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "filtering examples:   0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e4d0cd48554453ebfc855e71d3f9ad8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "selecting classes:   0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7ea94649123a4210a40f3819a6ac4eac",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "gather examples:   0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 11ms/step\n",
      "\n",
      "Performing NN search\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e23e0b3e437f4668bcf36002e7e9b1f6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Building NN list:   0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# re-run to test on other examples\n",
    "num_neighbors = 5\n",
    "\n",
    "# select\n",
    "x_display, y_display = tfsim.samplers.select_examples(x_test, y_test, CLASSES, 1)\n",
    "\n",
    "# lookup the nearest neighbors\n",
    "nns = model.lookup(x_display, k=num_neighbors)\n",
    "\n",
    "# display\n",
    "for idx in np.argsort(y_display):\n",
    "    tfsim_visualization.viz_neigbors_imgs(x_display[idx], y_display[idx], nns[idx], fig_size=(16, 2), cmap=\"Greys\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thanks you for following this tutorial till the end. If you are interested in learning about TensorFlow Similarity advanced features, you can checkout our other notebooks."
   ]
  }
 ],
 "metadata": {
  "environment": {
   "kernel": "python3",
   "name": "tf2-gpu.2-8.m91",
   "type": "gcloud",
   "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-8:m91"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  },
  "metadata": {
   "interpreter": {
    "hash": "beb34c979f53df918eee6ac62e82d9c0beb9e6590c67a50c4f7c01f004ba70dc"
   }
  },
  "vscode": {
   "interpreter": {
    "hash": "c7b3389fe492617a9dd862ce9e69c0dfb3e62254a4be8ff5561f4f9b5c78139c"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
